\pdfbookmark[0]{Tables}{tables}

```{=latex}
\tablepdf
    {rent_association} % label
    {Association Between Rent and Changes in Business Revenue and Employment} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {1} % page number in PDF
%%%%% Notes %%%%%
    {This table presents estimates from population-weighted OLS regressions at the county and ZIP-level. We regress percentage changes in small business revenue (using Womply data) and low-wage employment (using Paychex-Intuit and Earnin data) on median two-bedroom rent (as measured in the 2014-2018 ACS). Standard errors are reported in parentheses; county-level regressions use robust standard errors and ZIP-level regressions use standard errors clustered by county. The dependent variable is in percentage point units. The dependent variable in Panel A is the average change in small business revenue measured from March 23 to April 12, 2020, relative to January 4 to 31, 2020. All regressions in Panel A are at the ZIP-code level. The dependent variable in Panel B is the change in low-wage employment measured from June 27 to July 31, 2020, relative to January 4 to 31, 2020. In Panel B, Column (1) and (2) are at the county level using combined Paychex and Intuit data, while Column (3) is at the ZIP code level using Earnin data. In both panels, Column (1) shows the baseline regression without any controls: this specification corresponds to the estimated slope coefficient and standard error reported in Figure \ref{fig:smallbiz_zip_associations} (small business revenue) and Figure \ref{fig:employment_vs_rent} (low-wage employment). In Panel A, Column (2) adds county fixed effects and Column (3) further adds the log of the density of high wage workers as a control (which is observed using the Census LODES for 92\% of ZIP codes representing 99\% of the U.S. population). In Panel B, Column (2) adds the log of the density of high wage workers as a control to the baseline county level regression, while Column (3) switches to ZIP code level data for a specification analogous to the one in Column (3) of Panel A. Data sources: Womply, Paychex, Intuit, Earnin, ACS, Census LODES.}
    {}
```

```{=latex}
\tablepdf
    {low_wage_reduction} % label
    {Mechanisms Underlying the Persistent Reduction in Low-Wage Employment: Hysteresis vs. Current Conditions} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {2} % page number in PDF
%%%%% Notes %%%%%
    {This table presents estimates for a set of population-weighted regressions examining the determinants of employment patterns in December 2021 at the county level. Robust standard errors are reported in parentheses. The sample omits California, Massachusetts, and New York due to mismeasurement of low-wage employment changes as a result of minimum wage increases; see Appendix \ref{subsec:Internal-Data-Processing} for more information. Column (1) reports the results of regressing the change in low-wage (i.e. bottom quartile) employment from January 2020 to December 2021 against the average median two-bedroom rent (as measured in the 2014-2018 ACS) at the county level. Column (2) adds the average COVID-19 case rate in October to December 2021 (a measure of the risk of COVID exposure), the maximum number of weeks of unemployment insurance eligibility in each state, and a set of demographic controls: foreign-born population share, non-white population share, share of the population who are working age (25-54), and female population share. Column (3) repeats the specification in Column (2), replacing median two-bedroom rent with the size of the initial low-wage employment shock to each county, measured as the change in low-wage employment from January 2020 to July 2020. Column (4) repeats the specification in Column (2) using the change in high-wage (i.e. top quartile) employment from January 2020 to December 2021 as the dependent variable. The bottom two rows of the table report the change in the dependent variable explained by COVID risk exposure and UI extensions, calculated by multiplying the coefficient by the population-weighted mean of the respective variable. Data sources: Paychex, Intuit.}
    {}
```